Abstract: Systems and methods training a model are disclosed. In the method, training data is obtained by a deep neural network (DNN) first, the deep neural network comprising at least one hidden layer. Then features of the training data are obtained from a specified hidden layer of the at least one hidden layer, the specified hidden layer being connected respectively to a supervised classification network for classification tasks and an autoencoder based reconstruction network for reconstruction tasks.

Abstract: A system for tracking cumulative motor vehicle risk includes a satellite navigation system receiver disposed within a motor vehicle and configured to determine a present location of the motor vehicle. A computer processor receives the determined present location of the motor vehicle from the satellite navigation system receiver and generates a traveled route therefrom. A first computer server receives a plurality of motor vehicle claims records, determines a plurality of motor vehicle accident locations from the plurality of motor vehicle claims records, and generates a motor vehicle accident heat map from the plurality of motor vehicle accident locations. A second computer server determines a cumulative risk exposure of the motor vehicle based on the generated traveled route and the generated motor vehicle accident heat map.

Abstract: A method according to the present invention includes predicting a vehicular route. GPS data of a vehicle's position on a road network is received. A digital map representing the road network is received. The digital map includes a plurality of partitioned regions. Each of the partitioned regions includes a plurality of geographic nodes. A starting node is selected. At least one partitioned region is selected based on a predetermined travel-time horizon of the vehicle from the starting node. Route simulation is performed between the plurality of geographic nodes of the selected at least one partitioned region and a plurality of potential future routes is generated. An actual route of the vehicle is detected. The actual route of the vehicle is compared with the plurality of potential future routes. A probability of the vehicle traveling along each potential future route is determined. A future route of the vehicle is predicted.

Abstract: Systems and methods for obtaining vehicle operational data and driving context data from one or more monitoring systems, including converting the obtained vehicle operational data and driving context data into sequential vehicle operational feature data and sequential driving context feature data, calibrating the sequential vehicle operational feature data and the sequential driving context feature data temporally to form calibrated sequential vehicle operational feature data and calibrated sequential driving context feature data, constructing a sequence table of temporal sample points based on the calibrated sequential vehicle operational feature data and the calibrated sequential driving context feature data, feeding the sequence table into a deep neural network model for applying network learning to form a trained deep neural network model, extracting driving behavior features from the trained deep neural network model and analyzing the extracted driving behavior features to determine driving behavior char

Abstract: A method and system obtaining positioning data from an object traveling on a plurality of routes; mapping the data into a plurality of points on a digital map; identifying points that are unmatched to the stored route trajectory; obtaining candidate transition points from the unmatched points; aggregating the candidate transition points by applying a clustering algorithm; selecting a first cluster of points and a plurality of second clusters of points, determining a confidence level that the first cluster of points are transition points indicating a transition between the routes, classifying the first cluster of points as a first plurality of traveling points having a first direction in response to the confidence being below a threshold confidence and automatically adjusting the stored route trajectory to indicate that the first cluster of points are on the route trajectory.

Abstract: Embodiments of the present invention disclose a technical solution of determining a border between road network partitions, comprising: determining a border point based on road network data and partitioning information of road segments in the road network; determining a buffer zone between adjacent partitions based on the border point, the partitioning information of the road segments, and the road network data; determining a border between the adjacent partitions based on the buffer zone. With the solution of the present invention, border lines for road network partitions can be accurately determined.

Abstract: The disclosure involves a method comprising clustering a plurality of observation samples related to historical travel demands into one or more clusters; for each cluster, constructing an actual probability distribution of the historical travel demands corresponding to the observation samples in the cluster; for each cluster, inputting observation samples in the cluster into a prediction model for predicting future travel demands to produce a result of prediction; for each cluster, computing a predicted probability distribution of the historical travel demands corresponding to the observation samples in the cluster based on the result of prediction; for each cluster, evaluating a difference between the actual probability distribution and the predicted probability distribution of the cluster; and modifying the prediction model so that a statistical sum of the differences for the one or more clusters is decreased.

Abstract: The disclosure involves a method comprising clustering a plurality of observation samples related to historical travel demands into one or more clusters; for each cluster, constructing an actual probability distribution of the historical travel demands corresponding to the observation samples in the cluster; for each cluster, inputting observation samples in the cluster into a prediction model for predicting future travel demands to produce a result of prediction; for each cluster, computing a predicted probability distribution of the historical travel demands corresponding to the observation samples in the cluster based on the result of prediction; for each cluster, evaluating a difference between the actual probability distribution and the predicted probability distribution of the cluster; and modifying the prediction model so that a statistical sum of the differences for the one or more clusters is decreased.

Abstract: A method of tagging a geographical area includes obtaining, with a processing device, attribute information and mobile tracking data of a plurality of mobile objects, wherein the mobile tracking data comprises sampling time and corresponding sampling point locations of the mobile objects; converting the mobile tracking data of the plurality of mobile objects into new mobile tracking data according to the correspondence relationship between the sampling time and a time slices, wherein the new mobile tracking data include time slices and corresponding sampling point locations; and obtaining a set of attribute information of at least one geographical area with respect to the time slices based on the new mobile tracking data, wherein the at least one geographical area is obtained by clustering the sampling point locations.

Abstract: A method, system, and computer program product for obtaining a first route traversed by a target object, performing at least one prediction for a second route to be traversed by the target object based on the first route, the at least one prediction being performed with at least one of an object-specific prediction model, an object group-specific prediction model, and an object-independent prediction model, and determining, according to a decision rule, a prediction result of the second route based on the at least one prediction.

Abstract: A method, system, and computer program product, include receiving a plurality of requests for dynamic context information from a plurality of road segments, determining whether the plurality of road segments are included in a same cluster of road segments in a road network generated by clustering road segments in the road network based on connectivity of the road network, and consolidating the plurality of requests to generate a consolidated request in response to determining that the plurality of road segments are included in the same cluster.

Abstract: A method according to the present invention includes predicting a vehicular route. GPS data of a vehicle's position on a road network is received. A digital map representing the road network is received. The digital map includes a plurality of partitioned regions. Each of the partitioned regions includes a plurality of geographic nodes. A starting node is selected. At least one partitioned region is selected based on a predetermined travel-time horizon of the vehicle from the starting node. Route simulation is performed between the plurality of geographic nodes of the selected at least one partitioned region and a plurality of potential future routes is generated. An actual route of the vehicle is detected. The actual route of the vehicle is compared with the plurality of potential future routes. A probability of the vehicle traveling along each potential future route is determined. A future route of the vehicle is predicted.

Abstract: A method, system, and computer program product, include receiving a plurality of requests for dynamic context information from a plurality of road segments, determining whether the plurality of road segments are included in a same cluster of road segments in a road network generated by clustering road segments in the road network based on connectivity of the road network, and consolidating the plurality of requests to generate a consolidated request in response to determining that the plurality of road segments are included in the same cluster.

Abstract: A method according to the present invention includes receiving first vehicle sensor data. The first vehicle sensor data includes first location data and first camera data. The first vehicle sensor data is joined with an existing data cluster of second vehicle sensor data corresponding to additional vehicles. The second vehicle sensor data includes second location data and second camera data. A vehicle heading sequence of the first vehicle is determined, and additional vehicle heading sequences of the additional vehicles based on the second vehicle sensor data is determined. A positional relationship between the vehicle heading sequence of the first vehicle and a target object, and additional positional relationships between each of the additional vehicle heading sequences and the target object are determined. The existing data cluster is split into a plurality of data sub-clusters based on similarities between the positional relationship and the additional positional relationships.

Abstract: A system, a computer readable storage medium, and a method for detecting insurance fraud or for comparing references use visual analytics-based techniques. The method can include identifying a scratch and a scratch location on a vehicle in a 3-dimensional rendering in comparison to a base image or in comparison to a stored image in a database, comparing one or more features of the scratch such as color features of the scratch in the scratch location in comparison to the scratch location or comparing one or more texture features of the scratch in the scratch location in comparison to the scratch location in the base image or the stored image. The method can further generate a similarity calculation based on the one or more comparisons for the scratch, the scratch location, the one or more scratch features of the scratch and presents a result in response to the similarity calculation.

Abstract: Embodiments are described for minimizing a wait time for a rider after sending a ride request for a vehicle. An example computer-implemented method includes receiving a ride request, the request being for travel from a starting location to a zone in a geographic region during a specified timeslot. The method further includes predicting travel demand based on a number of ride requests in the zone during the specified timeslot. The method further includes requesting transport of one or more vehicles to the zone in response to the predicted number of ride requests when the travel demand is predicted to exceed a number of vehicles in the zone during the specified timeslot.

Abstract: Embodiments are described for minimizing a wait time for a rider after sending a ride request for a vehicle. An example computer-implemented method includes receiving a ride request, the request being for travel from a starting location to a zone in a geographic region during a specified timeslot. The method further includes predicting travel demand based on a number of ride requests in the zone during the specified timeslot. The method further includes requesting transport of one or more vehicles to the zone in response to the predicted number of ride requests when the travel demand is predicted to exceed a number of vehicles in the zone during the specified timeslot.

Abstract: Disclosed are a computer-implemented method for generating a data cube from data, a system and a computer program product. The method comprises selecting a candidate granularity from a plurality of candidate granularities determined for a dimension of the data cube, where a data distribution obtained in the selected candidate granularity satisfies a predetermined condition; and generating the data cube based on the selected candidate granularity for the dimension.

Abstract: Systems and methods for obtaining vehicle operational data and driving context data from one or more monitoring systems, including converting the obtained vehicle operational data and driving context data into sequential vehicle operational feature data and sequential driving context feature data, calibrating the sequential vehicle operational feature data and the sequential driving context feature data temporally to form calibrated sequential vehicle operational feature data and calibrated sequential driving context feature data, constructing a sequence table of temporal sample points based on the calibrated sequential vehicle operational feature data and the calibrated sequential driving context feature data, feeding the sequence table into a deep neural network model for applying network learning to form a trained deep neural network model, extracting driving behavior features from the trained deep neural network model and analyzing the extracted driving behavior features to determine driving behavior char

Abstract: A method, system, and computer program product, include receiving a plurality of requests for dynamic context information from a plurality of road segments, determining whether the plurality of road segments are included in a same cluster of road segments in a road network generated by clustering road segments in the road network based on connectivity of the road network, and consolidating the plurality of requests to generate a consolidated request in response to determining that the plurality of road segments are included in the same cluster.